A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

基于粒子群算法的宽带真延时平面阵列方向图综合

针对大阵元间距平面阵列方向图综合问题,提出基于粒子群算法的宽带真延时平面阵列方向图综合方法。该方法根据真延时条件下阵列方向图主瓣和栅瓣指向的特点,采用宽带真延时抑制阵列方向图的栅瓣；利用粒子群优化算法优化阵列结构,得到具有较高主副瓣比的平面阵列方向图。通过对8×8的矩形平面阵进行仿真,验证了所提方法的有效性,在此基础上,还仿真研究了该方法对栅瓣的抑制性能与信号带宽之间的关系。     

基于功率谱的舰船水压信号检测方法研究

为了有效地从风浪背景下检测舰船水压场信号,鉴于舰船水压场信号与海浪水压信号在频谱上的差异,采用求卓越频率下的功率谱的方法对信号进行检测,取窗宽20s,每次计算21个采样点,分别求取海浪水压场的仿真信号及不同波浪级别下海上某目标的水压场实测数据的功率谱进行信号检测,验证了方法的有效性。     

一种提高智能光轴检测系统定位精度的方法

针对目前各种光轴检测装置普遍存在精度不高的问题 ,基于CCD智能光轴检测系统 ,提出了一种细分插值的算法。实验表明 ,该算法的软件实现可以将像点的位置坐标提高到亚微米级。     

导弹毁伤机场目标的信息处理技术

立足现实作战需求 ,运用多种图像处理方法和模式识别方法 ,研究目标实际毁伤信息的综合提取和处理方法 ,重点研究机场类目标的毁伤信息处理技术 ,建立了该类典型目标的打击效果判定系统。     

DSP实现低信噪比条件下点目标检测与跟踪

针对嵌入式实时图像处理系统中 ,复杂背景低信噪比条件下红外运动点目标检测及跟踪所采用的处理算法 ,包括基于空域低通滤波的多图像平均法和自适应阈值分割法以及基于目标运动时空连续性目标运动轨迹拟合及目标运动轨迹参数预测算法 ,进行基于二维数组组合方式存储结构的算法复杂度估计 ,并将上述算法在TI公司的TMS32 0C6 2 0 1EVM板上进行实时性能评测 ,说明上述算法在基于具有软件流水性能的TMS32 0C6x系列DSP嵌入式实时图像处理系统中实现的可行性。